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# Second Moment Convergence Rates for Uniform Empirical Processes

*Journal of Inequalities and Applications*
**volumeÂ 2010**, ArticleÂ number:Â 972324 (2010)

## Abstract

Let be a sequence of independent and identically distributed -distributed random variables. Define the uniform empirical process as , . In this paper, we get the exact convergence rates of weighted infinite series of .

## 1. Introduction and Main Results

Let be a sequence of independent and identically distributed (i.i.d.) random variables with zero mean. Set for , and . Hsu and Robbins [1] introduced the concept of complete convergence. They showed that

if and . The converse part was proved by the study of ErdÃ¶s in [2]. Obviously, the sum in (1.1) tends to infinity as . Many authors studied the exact rates in terms of (cf. [3â€“5]). Chow [6] studied the complete convergence of , . Recently, Liu and Lin [7] introduced a new kind of complete moment convergence which is interesting, and got the precise rate of it as follows.

Theorem A.

Suppose that is a sequence of i.i.d. random variables, then

holds, if and only if , , and .

Other than partial sums, many authors investigated precise rates in some different cases, such as *U*-statistics (cf. [8, 9]) and self-normalized sums (cf. [10, 11]). Zhang and Yang [12] extended the precise asymptotic results to the uniform empirical process. We suppose is the sample of random variables and is the empirical distribution function of it. Denote the uniform empirical process by , , and the norm of a function on by . Let , be the Brownian bridge. We present one result of Zhang and Yang [12] as follows.

Theorem B.

For any , one has

Inspired by the above conclusions, we consider second moment convergence rates for the uniform empirical process in the law of iterated logarithm and the law of the logarithm. Throughout this paper, let denote a positive constant whose values can be different from one place to another. will denote the largest integer . The following two theorems are our main results.

Theorem 1.1.

For , one has

Theorem 1.2.

For , one has

Remark 1.3.

It is well known that , (see CsÃ¶rgÅ‘ and RÃ©vÃ©sz [13, page 43]). Therefore, by Fubini's theorem we have

Consequently, explicit results of (1.4) and (1.5) can be calculated further.

## 2. The Proofs

In order to prove Theorem 1.1, we present several propositions first.

Proposition 2.1.

For , , one has

Proof.

We calculate that

Proposition 2.2.

For , one has

Proof.

Following [4], set , where . Write

It is wellknown that (see CsÃ¶rgÅ‘ and RÃ©vÃ©sz [13, page 17]). By continuous mapping theorem, we have . As a result, it follows that

Using the Toeplitz's lemma (see Stout [14, pages 120-121]), we can get . For , it is obvious that

Notice that , for a small . Via the similar argument in [4] we have

From Kiefer and Wolfowitz [15], we have

Therefore,

From (2.6), (2.7), and (2.9), we get . Proposition 2.2 has been proved.

Proposition 2.3.

For , one has

Proof.

The calculation here is analogous to (2.1), so it is omitted here.

Proposition 2.4.

For , one has

Proof.

Like [4] and Proposition 2.2, we divide the summation into two parts,

First, consider ,

Since means , it follows

By Lemma in Zhang and Yang [12], we have . For , it is easy to get

In the same way, by the inequality , we can get

Put the three parts together, we get that uniformly in as . Using Toeplitz's lemma again, we have

In the sequel, we verify . It is easy to see that

We estimate first, by noticing and (2.8), it follows

Therefore, we get . So far, we only need to prove . Use the inequality again and follow the proof of , we can get this result. The proof of the proposition is completed now.

Proof of Theorem 1.1.

According to Fubini's theorem, it is easy to get

for . Therefore, we have

From Proposition 2.1â€“ 2.4, we have

Proof of Theorem 1.2.

From (2.19), we have

Via the similar argument in Proposition 2.1 and 2.2,

Also, by the analogous proof of Proposition 2.3 and 2.4,

Combine (2.22), (2.23), and (2.24)together, we get the result of Theorem 1.2.

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## Acknowledgment

This work was supported by NSFC (No. 10771192) and ZJNSF (No. J20091364).

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Chen, YY., Zhang, LX. Second Moment Convergence Rates for Uniform Empirical Processes.
*J Inequal Appl* **2010**, 972324 (2010). https://doi.org/10.1155/2010/972324

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DOI: https://doi.org/10.1155/2010/972324